3 Search Results for "Nakov, Georgi"

Document
Survey
DOI: 10.4230/TGDK.1.1.7
Rule Learning over Knowledge Graphs: A Review

Authors: Hong Wu, Zhe Wang, Kewen Wang, Pouya Ghiasnezhad Omran, and Jiangmeng Li

Published in: TGDK, Volume 1, Issue 1 (2023): Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 1, Issue 1

Abstract
Compared to black-box neural networks, logic rules express explicit knowledge, can provide human-understandable explanations for reasoning processes, and have found their wide application in knowledge graphs and other downstream tasks. As extracting rules manually from large knowledge graphs is labour-intensive and often infeasible, automated rule learning has recently attracted significant interest, and a number of approaches to rule learning for knowledge graphs have been proposed. This survey aims to provide a review of approaches and a classification of state-of-the-art systems for learning first-order logic rules over knowledge graphs. A comparative analysis of various approaches to rule learning is conducted based on rule language biases, underlying methods, and evaluation metrics. The approaches we consider include inductive logic programming (ILP)-based, statistical path generalisation, and neuro-symbolic methods. Moreover, we highlight important and promising application scenarios of rule learning, such as rule-based knowledge graph completion, fact checking, and applications in other research areas.
Cite as

Hong Wu, Zhe Wang, Kewen Wang, Pouya Ghiasnezhad Omran, and Jiangmeng Li. Rule Learning over Knowledge Graphs: A Review. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 7:1-7:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{wu_et_al:TGDK.1.1.7,
  author =	{Wu, Hong and Wang, Zhe and Wang, Kewen and Omran, Pouya Ghiasnezhad and Li, Jiangmeng},
  title =	{{Rule Learning over Knowledge Graphs: A Review}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{7:1--7:23},
  ISSN =	{2942-7517},
  year =	{2023},
  volume =	{1},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.7},
  URN =		{urn:nbn:de:0030-drops-194813},
  doi =		{10.4230/TGDK.1.1.7},
  annote =	{Keywords: Rule learning, Knowledge graphs, Link prediction}
}
Document
DOI: 10.4230/LIPIcs.TYPES.2021.10
Quantitative Polynomial Functors

Authors: Georgi Nakov and Fredrik Nordvall Forsberg

Published in: LIPIcs, Volume 239, 27th International Conference on Types for Proofs and Programs (TYPES 2021)

Abstract
We investigate containers and polynomial functors in Quantitative Type Theory, and give initial algebra semantics of inductive data types in the presence of linearity. We show that reasoning by induction is supported, and equivalent to initiality, also in the linear setting.
Cite as

Georgi Nakov and Fredrik Nordvall Forsberg. Quantitative Polynomial Functors. In 27th International Conference on Types for Proofs and Programs (TYPES 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 239, pp. 10:1-10:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{nakov_et_al:LIPIcs.TYPES.2021.10,
  author =	{Nakov, Georgi and Nordvall Forsberg, Fredrik},
  title =	{{Quantitative Polynomial Functors}},
  booktitle =	{27th International Conference on Types for Proofs and Programs (TYPES 2021)},
  pages =	{10:1--10:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-254-9},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{239},
  editor =	{Basold, Henning and Cockx, Jesper and Ghilezan, Silvia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TYPES.2021.10},
  URN =		{urn:nbn:de:0030-drops-167795},
  doi =		{10.4230/LIPIcs.TYPES.2021.10},
  annote =	{Keywords: quantitative type theory, polynomial functors, inductive data types}
}
Document
Early Ideas
DOI: 10.4230/LIPIcs.CALCO.2021.22
Quantitative Polynomial Functors (Early Ideas)

Authors: Georgi Nakov and Fredrik Nordvall Forsberg

Published in: LIPIcs, Volume 211, 9th Conference on Algebra and Coalgebra in Computer Science (CALCO 2021)

Abstract
We investigate containers and polynomial functors in Quantitative Type Theory, and give initial algebra semantics of inductive data types in the presence of linearity. We show that reasoning by induction is supported, and equivalent to initiality, also in the linear setting.
Cite as

Georgi Nakov and Fredrik Nordvall Forsberg. Quantitative Polynomial Functors (Early Ideas). In 9th Conference on Algebra and Coalgebra in Computer Science (CALCO 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 211, pp. 22:1-22:5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{nakov_et_al:LIPIcs.CALCO.2021.22,
  author =	{Nakov, Georgi and Nordvall Forsberg, Fredrik},
  title =	{{Quantitative Polynomial Functors}},
  booktitle =	{9th Conference on Algebra and Coalgebra in Computer Science (CALCO 2021)},
  pages =	{22:1--22:5},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-212-9},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{211},
  editor =	{Gadducci, Fabio and Silva, Alexandra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CALCO.2021.22},
  URN =		{urn:nbn:de:0030-drops-153774},
  doi =		{10.4230/LIPIcs.CALCO.2021.22},
  annote =	{Keywords: quantitative type theory, polynomial functors, inductive data types}
}
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